Job Forecasting Based on the Patent Information: A Word Embedding-Based Approach

Abstract

The rapid change in technology makes it challenging to forecast the future of jobs. Previous studies have analyzed economics and employment data or employed expert-based methods to forecast the future of jobs, but these approaches were not able to reflect the latest technology trends in an objective way. To overcome the issue, this study matches jobs with patents and forecasts the future of jobs based on changes in the number of patents with time. A word embedding model is trained by patent classification code and job description data and used to find similar patent classification codes of jobs. For an illustration purpose, we identify information technology-related jobs listed in O∗NET and discover similar patent classification codes of the jobs. Based on the change in the number of patents, we find promising jobs presenting high technical demands. Several implications of our approach are also discussed.